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Using Power BI to Automate Data Cleaning and Visualization

Using Power BI to Automate Data Cleaning and Visualization. Frederick Burrack Director of Assessment Office of Assessment. Chris Urban Assistant Director of Data Analytics Planning & Analysis. Visualizing Data through Interactive Reports in Power BI. Data Dashboards. Survey Results

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Using Power BI to Automate Data Cleaning and Visualization

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  1. Using Power BI to Automate Data Cleaning and Visualization

  2. Frederick Burrack Director of Assessment Office of Assessment Chris Urban Assistant Director of Data Analytics Planning & Analysis

  3. Visualizing Data through Interactive Reports in Power BI

  4. Data Dashboards

  5. Survey Results • Form Entries Data Visualization Tool • Assessment Results • Engagement/Clicks Combined, interactive dashboards and reports • Documents • Data Warehouse • Student Demographics • Enrollments

  6. Relational Data Modeling

  7. Facts Dimensions Contains items you want to identify: Sum, average, count, etc. Contains fields to break down a Fact Table

  8. Facts Dimensions Long and narrow Duplicated Short and wide Unduplicated

  9. Facts Dimensions DimStudent[StudentID] FactResponses[StudentID] Dimensions relate to Facts. Used as a filter via Key Fields.

  10. Dimensions that surround a Fact Table are called a “Star Schema”

  11. Automating Data Processes

  12. Power BI Suite Power BI Service Power BI Desktop PUBLISH ACCESS Query and Report Creation Power BI Gateways Your Institution’s Data Adapted from Microsoft.com

  13. Step-by-Step Demonstration

  14. Data Analysis Expressions An Introduction to DAX

  15. Data Analysis Expressions (DAX) • Functions used to create reusable measures that analyze data • Basic commands such as COUNT, SUM, AVERAGE, etc. • Generally used in Fact tables to aggregate • Can reference other DAX formulas – no need to re-enter data

  16. A Basic Measure using DAX Function Column Used to Calculate Count Responses = COUNT(FactResponses[Response]) Name of the Measure Table used to calculate

  17. Count Responses = COUNT(FactResponses[Response])

  18. Count All Responses = CALCULATE([Count Responses], ALL(DimResponse))

  19. %Responses = [Count Responses] / [Count All Responses]

  20. Step-by-Step Demonstration

  21. Count Responses = COUNT(FactResponses[Response]) Count All Responses = CALCULATE([Count Responses], ALL(DimResponse)) %Responses = [Count Responses] / [Count All Responses] DAX Measures to count Responses

  22. Count Students = DISTINCTCOUNT(FactResponses[student id]) Count All Students = CALCULATE([Count Students], ALL(DimStudent)) %Students = [Count Students] / [Count All Students] DAX Measures to count students

  23. Publishing and Sharing Power BI Service Power BI Desktop PUBLISH ACCESS Query and Report Creation Power BI Gateways Your Institution’s Data Adapted from Microsoft.com

  24. Sharing Options

  25. Request a Pro License ($25/user/yr):https://www.k-state.edu/its/software/software-licenses/ms-power-bi/

  26. K-State Power BI Users Groupemail Chuck Gould – chuck@ksu.edu K-State Power BI Slack Channel ksupowerbi.slack.com

  27. What about the data warehouse?

  28. Resource Documents

  29. Questions & Discussion Thanks for coming! Using Power BI to Automate Data Cleaning and Visualization

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